At the recently held annual Cloud Next event, Google Cloud announced the beta availability of Confidential Virtual Machines (VMs) for Google Compute Engine powered by 2nd Gen AMD EPYC processors.

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The first product in the Google Cloud Confidential Computing portfolio, Confidential VMs, enables customers for the first time to encrypt data in-use while it is being processed and not just when at rest and in-transit, the company said.

The Confidential VMs from Google provide real time encryption-in-use; Google Cloud customers can encrypt data-in-use, taking advantage of advanced security features offered by the 2nd Gen AMD EPYC processor together with Confidential Computing cloud services.

Secure Encrypted Virtualization (SEV), an advanced security feature available on AMD EPYC processors, encrypts VM memory using a dedicated per-VM key that is generated and managed by the embedded security processor.

AMD and Google said that they have simplified the use of Confidential Computing, making the transition to Confidential VMs seamless as customers do not need to make any code changes to their applications to benefit from these VMs.

“By using advanced security technology in the AMD EPYC processors, we’ve created a breakthrough technology that allows customers to encrypt their data in the cloud while it’s being processed and unlock computing scenarios that had previously not been possible,” said Vint Cerf, vice president and chief internet evangelist, Google.

Dan McNamara, senior vice president and general manager, Server Business Unit, AMD, added: “To help provide the confidence that customers can move their sensitive workloads to the cloud, AMD and Google worked together on the Google Confidential VMs to take advantage of an advanced security feature, Secure Encrypted Virtualization, within AMD EPYC processors. This helps enable a unified and consistent level of hardware-based security for applications and workloads in the cloud. As well, AMD and Google have worked together to help customers both secure their data and achieve high performance of their workloads.”

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